

"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class      _UpperCAmelCase   (							_lowerCAmelCase		,    unittest.TestCase       ):
	a__		: str					=					KandinskyInpaintPipeline
	a__		: Tuple					=					["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	a__		: List[str]					=					[
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	a__		: Optional[Any]					=					[
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	a__		: List[Any]					=					False



	@property
	def        a  (  self     :       Optional[int]						):
		return 32



	@property
	def        a  (  self     :       List[str]						):
		return 32



	@property
	def        a  (  self     :       Union[str, Any]						):
		return self.time_input_dim



	@property
	def        a  (  self     :       Union[str, Any]						):
		return self.time_input_dim * 4



	@property
	def        a  (  self     :       Union[str, Any]						):
		return 1_00



	@property
	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''						)
		return tokenizer



	@property
	def        a  (  self     :       List[str]						):
		torch.manual_seed(0						)
		__UpperCAmelCase      		= MCLIPConfig(
		    numDims=self.cross_attention_dim			,    transformerDimensions=self.text_embedder_hidden_size			,    hidden_size=self.text_embedder_hidden_size			,    intermediate_size=37			,    num_attention_heads=4			,    num_hidden_layers=5			,    vocab_size=10_05			,    )

		__UpperCAmelCase      		= MultilingualCLIP(_lowercase						)
		__UpperCAmelCase      		= text_encoder.eval()

		return text_encoder



	@property
	def        a  (  self     :       Optional[int]						):
		torch.manual_seed(0						)

		__UpperCAmelCase      		= {
		    '''in_channels''': 9,
		    # Out channels is double in channels because predicts mean and variance
		    '''out_channels''': 8,
		    '''addition_embed_type''': '''text_image''',
		    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
		    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
		    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
		    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
		    '''layers_per_block''': 1,
		    '''encoder_hid_dim''': self.text_embedder_hidden_size,
		    '''encoder_hid_dim_type''': '''text_image_proj''',
		    '''cross_attention_dim''': self.cross_attention_dim,
		    '''attention_head_dim''': 4,
		    '''resnet_time_scale_shift''': '''scale_shift''',
		    '''class_embed_type''': None,
		}

		__UpperCAmelCase      		= UNetaDConditionModel(**_lowercase						)
		return model



	@property
	def        a  (  self     :       List[str]						):
		return {
		    "block_out_channels": [32, 64],
		    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
		    "in_channels": 3,
		    "latent_channels": 4,
		    "layers_per_block": 1,
		    "norm_num_groups": 8,
		    "norm_type": "spatial",
		    "num_vq_embeddings": 12,
		    "out_channels": 3,
		    "up_block_types": [
		        "AttnUpDecoderBlock2D",
		        "UpDecoderBlock2D",
		    ],
		    "vq_embed_dim": 4,
		}



	@property
	def        a  (  self     :       int						):
		torch.manual_seed(0						)
		__UpperCAmelCase      		= VQModel(**self.dummy_movq_kwargs						)
		return model



	def        a  (  self     :       List[Any]						):
		__UpperCAmelCase      		= self.dummy_text_encoder
		__UpperCAmelCase      		= self.dummy_tokenizer
		__UpperCAmelCase      		= self.dummy_unet
		__UpperCAmelCase      		= self.dummy_movq

		__UpperCAmelCase      		= DDIMScheduler(
		    num_train_timesteps=10_00			,    beta_schedule='''linear'''			,    beta_start=0.00_085			,    beta_end=0.012			,    clip_sample=_lowercase			,    set_alpha_to_one=_lowercase			,    steps_offset=1			,    prediction_type='''epsilon'''			,    thresholding=_lowercase			,    )

		__UpperCAmelCase      		= {
		    '''text_encoder''': text_encoder,
		    '''tokenizer''': tokenizer,
		    '''unet''': unet,
		    '''scheduler''': scheduler,
		    '''movq''': movq,
		}

		return components



	def        a  (  self     :       Union[str, Any]			,    _lowercase     :       List[Any]			,    _lowercase     :       Tuple=0						):
		__UpperCAmelCase      		= floats_tensor((1, self.cross_attention_dim)			,    rng=random.Random(_lowercase						)						).to(_lowercase						)
		__UpperCAmelCase      		= floats_tensor((1, self.cross_attention_dim)			,    rng=random.Random(seed + 1						)						).to(_lowercase						)
		# create init_image
		__UpperCAmelCase      		= floats_tensor((1, 3, 64, 64)			,    rng=random.Random(_lowercase						)						).to(_lowercase						)
		__UpperCAmelCase      		= image.cpu().permute(0			,    2			,    3			,    1						)[0]
		__UpperCAmelCase      		= Image.fromarray(np.uinta(_lowercase						)						).convert('''RGB'''						).resize((2_56, 2_56)						)
		# create mask
		__UpperCAmelCase      		= np.ones((64, 64)			,    dtype=np.floataa						)
		__UpperCAmelCase      		= 0

		if str(_lowercase						).startswith('''mps'''						):
			__UpperCAmelCase      		= torch.manual_seed(_lowercase						)
		else:
			__UpperCAmelCase      		= torch.Generator(device=_lowercase						).manual_seed(_lowercase						)
		__UpperCAmelCase      		= {
		    '''prompt''': '''horse''',
		    '''image''': init_image,
		    '''mask_image''': mask,
		    '''image_embeds''': image_embeds,
		    '''negative_image_embeds''': negative_image_embeds,
		    '''generator''': generator,
		    '''height''': 64,
		    '''width''': 64,
		    '''num_inference_steps''': 2,
		    '''guidance_scale''': 4.0,
		    '''output_type''': '''np''',
		}
		return inputs



	def        a  (  self     :       Union[str, Any]						):
		__UpperCAmelCase      		= '''cpu'''

		__UpperCAmelCase      		= self.get_dummy_components()

		__UpperCAmelCase      		= self.pipeline_class(**_lowercase						)
		__UpperCAmelCase      		= pipe.to(_lowercase						)

		pipe.set_progress_bar_config(disable=_lowercase						)

		__UpperCAmelCase      		= pipe(**self.get_dummy_inputs(_lowercase						)						)
		__UpperCAmelCase      		= output.images

		__UpperCAmelCase      		= pipe(
		    **self.get_dummy_inputs(_lowercase						)			,    return_dict=_lowercase			,    )[0]

		__UpperCAmelCase      		= image[0, -3:, -3:, -1]
		__UpperCAmelCase      		= image_from_tuple[0, -3:, -3:, -1]

		print(F'''image.shape {image.shape}'''						)

		assert image.shape == (1, 64, 64, 3)

		__UpperCAmelCase      		= np.array(
		    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]						)

		assert (
		    np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
		), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
		assert (
		    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
		), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



	def        a  (  self     :       Any						):
		super().test_inference_batch_single_identical(expected_max_diff=3E-3						)






@slow
@require_torch_gpu
class      _UpperCAmelCase   (							unittest.TestCase       ):



	def        a  (  self     :       List[str]						):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= load_numpy(
		    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
		    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''						)

		__UpperCAmelCase      		= load_image(
		    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''						)
		__UpperCAmelCase      		= np.ones((7_68, 7_68)			,    dtype=np.floataa						)
		__UpperCAmelCase      		= 0

		__UpperCAmelCase      		= '''a hat'''

		__UpperCAmelCase      		= KandinskyPriorPipeline.from_pretrained(
		    '''kandinsky-community/kandinsky-2-1-prior'''			,    torch_dtype=torch.floataa						)
		pipe_prior.to(_lowercase						)

		__UpperCAmelCase      		= KandinskyInpaintPipeline.from_pretrained(
		    '''kandinsky-community/kandinsky-2-1-inpaint'''			,    torch_dtype=torch.floataa						)
		__UpperCAmelCase      		= pipeline.to(_lowercase						)
		pipeline.set_progress_bar_config(disable=_lowercase						)

		__UpperCAmelCase      		= torch.Generator(device='''cpu'''						).manual_seed(0						)
		__UpperCAmelCase		,							__UpperCAmelCase      		= pipe_prior(
		    _lowercase			,    generator=_lowercase			,    num_inference_steps=5			,    negative_prompt=''''''			,    ).to_tuple()

		__UpperCAmelCase      		= pipeline(
		    _lowercase			,    image=_lowercase			,    mask_image=_lowercase			,    image_embeds=_lowercase			,    negative_image_embeds=_lowercase			,    generator=_lowercase			,    num_inference_steps=1_00			,    height=7_68			,    width=7_68			,    output_type='''np'''			,    )

		__UpperCAmelCase      		= output.images[0]

		assert image.shape == (7_68, 7_68, 3)

		assert_mean_pixel_difference(_lowercase			,    _lowercase						)

"""simple docstring"""



import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__:List[str]									=			direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE__:Optional[int]									=			re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE__:Optional[Any]									=			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE__:int									=			[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def 	_lowerCamelCase(	a				):
       __a       =    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"   ,    a				)
       return [m.group(0				) for m in matches]



def 	_lowerCamelCase(	):
       __a       =    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       __a       =    {
           config.replace("Config"   ,    ""				): model_type for model_type, config in config_maping_names.items()
       }

       # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)

       # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
       for attr_name in dir(a				):
              __a       =    None
              if _re_tf_models.match(a				) is not None:
                     __a       =    tf_models
                     __a       =    _re_tf_models.match(a				).groups()[0]
              elif _re_flax_models.match(a				) is not None:
                     __a       =    flax_models
                     __a       =    _re_flax_models.match(a				).groups()[0]
              elif _re_pt_models.match(a				) is not None:
                     __a       =    pt_models
                     __a       =    _re_pt_models.match(a				).groups()[0]

              if lookup_dict is not None:
                     while len(a				) > 0:
                            if attr_name in model_prefix_to_model_type:
                                   __a       =    True
                                   break
                            # Try again after removing the last word in the name
                            __a       =    "".join(camel_case_split(a				)[:-1]				)

       __a       =    set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
       __a       =    list(a				)
       all_models.sort()

       __a       =    {"model_type": all_models}
       __a       =    [pt_models[t] for t in all_models]
       __a       =    [tf_models[t] for t in all_models]
       __a       =    [flax_models[t] for t in all_models]

       # Now let's use the auto-mapping names to make sure
       __a       =    {}
       for t in all_models:
              if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                     __a       =    "AutoProcessor"
              elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
                     __a       =    "AutoTokenizer"
              elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
                     __a       =    "AutoFeatureExtractor"
              else:
                     # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                     __a       =    "AutoTokenizer"

       __a       =    [processors[t] for t in all_models]

       return pd.DataFrame(a				)



def 	_lowerCamelCase(	a				):
       __a       =    [
           transformers_module.models.auto.modeling_auto,
           transformers_module.models.auto.modeling_tf_auto,
           transformers_module.models.auto.modeling_flax_auto,
       ]
       for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
              __a       =    [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
              __a       =    [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
              # Loop through all three frameworks
              for module, cls, mapping in zip(a   ,    a   ,    a				):
                     # The type of pipeline may not exist in this framework
                     if not hasattr(a   ,    a				):
                            continue
                     # First extract all model_names
                     __a       =    []
                     for name in getattr(a   ,    a				).values():
                            if isinstance(a   ,    a				):
                                   model_names.append(a				)
                            else:
                                   model_names.extend(list(a				)				)

            # Add pipeline tag and auto model class for those models
                     table.update({model_name: (pipeline_tag, cls) for model_name in model_names}				)

       return table



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    get_frameworks_table()
       __a       =    Dataset.from_pandas(a				)

       __a       =    hf_hub_download(
           "huggingface/transformers-metadata"   ,    "pipeline_tags.json"   ,    repo_type="dataset"   ,    token=a				)
       __a       =    Dataset.from_json(a				)
       __a       =    {
           tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
           for i in range(len(a				)				)
       }
       __a       =    update_pipeline_and_auto_class_table(a				)

       # Sort the model classes to avoid some nondeterministic updates to create false update commits.
       __a       =    sorted(table.keys()				)
       __a       =    pd.DataFrame(
           {
               "model_class": model_classes,
               "pipeline_tag": [table[m][0] for m in model_classes],
               "auto_class": [table[m][1] for m in model_classes],
           }				)
       __a       =    Dataset.from_pandas(a				)

       with tempfile.TemporaryDirectory() as tmp_dir:
              frameworks_dataset.to_json(os.path.join(a   ,    "frameworks.json"				)				)
              tags_dataset.to_json(os.path.join(a   ,    "pipeline_tags.json"				)				)

              if commit_sha is not None:
                     __a       =    (
                         F"Update with commit {commit_sha}\n\nSee: "
                         F"https://github.com/huggingface/transformers/commit/{commit_sha}"
                     )
              else:
                     __a       =    "Update"

              upload_folder(
                  repo_id="huggingface/transformers-metadata"   ,    folder_path=a   ,    repo_type="dataset"   ,    token=a   ,    commit_message=a   ,    )



def 	_lowerCamelCase(	):
       __a       =    {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
       __a       =    transformers_module.pipelines.SUPPORTED_TASKS
       __a       =    []
       for key in pipeline_tasks:
              if key not in in_table:
                     __a       =    pipeline_tasks[key]["pt"]
                     if isinstance(a   ,    (list, tuple)				):
                            __a       =    model[0]
                     __a       =    model.__name__
                     if model not in in_table.values():
                            missing.append(a				)

       if len(a				) > 0:
              __a       =    ", ".join(a				)
              raise ValueError(
                  "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
                  F"`utils/update_metadata.py`: {msg}. Please add them!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]									=			argparse.ArgumentParser()
   parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
   parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
   parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()

   if args.check_only:
      check_pipeline_tags()
   else:
      update_metadata(args.token, args.commit_sha)
